2天学会Pandas
2天学会Pandas
公众号guangcity
发布于 2019-09-20 14:50:03
发布于 2019-09-20 14:50:03
2天学会Pandas
0.导语1.Series2.DataFrame2.1 DataFrame的简单运用3.pandas选择数据3.1 实战筛选3.2 筛选总结4.Pandas设置值4.1 创建数据4.2 根据位置设置loc和iloc4.3 根据条件设置4.4 按行或列设置4.5 添加Series序列(长度必须对齐)4.6 设定某行某列为特定值4.7 修改一整行数据5.Pandas处理丢失数据5.1 创建含NaN的矩阵5.2 删除掉有NaN的行或列5.3 替换NaN值为0或者其他5.4 是否有缺失数据NaN6.Pandas导入导出6.1 导入数据6.2 导出数据7.Pandas合并操作7.1 Pandas合并concat7.2.Pandas 合并 merge7.2.1 定义资料集并打印出7.2.2 依据key column合并,并打印7.2.3 两列合并7.2.4 Indicator设置合并列名称7.2.5 依据index合并7.2.6 解决overlapping的问题8.Pandas plot出图9.学习来源
0.导语
Pandas是基于Numpy构建的，让Numpy为中心的应用变得更加简单。
本文为一篇长文，建议收藏，转发~
1.Series
import pandas as pd
import numpy as np
# Series
s = pd.Series([1,3,6,np.nan,44,1])
print(s)
'''
0 1.0
1 3.0
2 6.0
3 NaN
4 44.0
5 1.0
dtype: float64
'''
# 默认index从0开始,如果想要按照自己的索引设置，则修改index参数,如:index=[3,4,3,7,8,9]
2.DataFrame
2.1 DataFrame的简单运用
# DataFrame
dates = pd.date_range('2018-08-19',periods=6)
# dates = pd.date_range('2018-08-19','2018-08-24') # 起始、结束 与上述等价
'''
numpy.random.randn(d0, d1, …, dn)是从标准正态分布中返回一个或多个样本值。
numpy.random.rand(d0, d1, …, dn)的随机样本位于[0, 1)中。
(6,4)表示6行4列数据
'''
df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])
print(df)
# DataFrame既有行索引也有列索引， 它可以被看做由Series组成的大字典。
print(df['b'])
'''
2018-08-19 -0.217424
2018-08-20 -1.421058
2018-08-21 -0.424589
2018-08-22 0.534675
2018-08-23 -0.018525
2018-08-24 0.635075
Freq: D, Name: b, dtype: float64
'''
# 未指定行标签和列标签的数据
df1 = pd.DataFrame(np.arange(12).reshape(3,4))
print(df1)
# 另一种方式
df2 = pd.DataFrame({
'A': [1,2,3,4],
'B': pd.Timestamp('20180819'),
'C': pd.Series([1,6,9,10],dtype='float32'),
'D': np.array([3] * 4,dtype='int32'),
'E': pd.Categorical(['test','train','test','train']),
'F': 'foo'
})
print(df2)
'''
A B C D E F
0 1 2018-08-19 1.0 3 test foo
1 2 2018-08-19 6.0 3 train foo
2 3 2018-08-19 9.0 3 test foo
3 4 2018-08-19 10.0 3 train foo
'''
print(df2.dtypes)
'''
A int64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object
'''
print(df2.index)
# RangeIndex(start=0, stop=4, step=1)
print(df2.columns)
# Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')
print(df2.values)
'''
[[1 Timestamp('2018-08-19 00:00:00') 1.0 3 'test' 'foo']
[2 Timestamp('2018-08-19 00:00:00') 6.0 3 'train' 'foo']
[3 Timestamp('2018-08-19 00:00:00') 9.0 3 'test' 'foo']
[4 Timestamp('2018-08-19 00:00:00') 10.0 3 'train' 'foo']]
'''
# 数据总结
print(df2.describe())
'''
A C D
count 4.000000 4.000000 4.0
mean 2.500000 6.500000 3.0
std 1.290994 4.041452 0.0
min 1.000000 1.000000 3.0
25% 1.750000 4.750000 3.0
50% 2.500000 7.500000 3.0
75% 3.250000 9.250000 3.0
max 4.000000 10.000000 3.0
'''
# 翻转数据
print(df2.T)
# print(np.transpose(df2))等价于上述操作
'''
axis=1表示行
axis=0表示列
默认ascending(升序)为True
ascending=True表示升序,ascending=False表示降序
下面两行分别表示按行升序与按行降序
'''
print(df2.sort_index(axis=1,ascending=True))
print(df2.sort_index(axis=1,ascending=False))
'''
A B C D E F
0 1 2018-08-19 1.0 3 test foo
1 2 2018-08-19 6.0 3 train foo
2 3 2018-08-19 9.0 3 test foo
3 4 2018-08-19 10.0 3 train foo
F E D C B A
0 foo test 3 1.0 2018-08-19 1
1 foo train 3 6.0 2018-08-19 2
2 foo test 3 9.0 2018-08-19 3
3 foo train 3 10.0 2018-08-19 4
'''
# 表示按列降序与按列升序
print(df2.sort_index(axis=0,ascending=False))
print(df2.sort_index(axis=0,ascending=True))
'''
A B C D E F
3 4 2018-08-19 10.0 3 train foo
2 3 2018-08-19 9.0 3 test foo
1 2 2018-08-19 6.0 3 train foo
0 1 2018-08-19 1.0 3 test foo
A B C D E F
0 1 2018-08-19 1.0 3 test foo
1 2 2018-08-19 6.0 3 train foo
2 3 2018-08-19 9.0 3 test foo
3 4 2018-08-19 10.0 3 train foo
'''
# 对特定列数值排列
# 表示对C列降序排列
print(df2.sort_values(by='C',ascending=False))
3.pandas选择数据
3.1 实战筛选
import pandas as pd
import numpy as np
dates = pd.date_range('20180819', periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C','D'])
print(df)
# 检索A列
print(df['A'])
print(df.A)
# 选择跨越多行或多列
# 选取前3行
print(df[0:3])
print(df['2018-08-19':'2018-08-21'])
'''
A B C D
2018-08-19 0 1 2 3
2018-08-20 4 5 6 7
2018-08-21 8 9 10 11
'''
# 根据标签选择数据
# 获取特定行或列
# 指定行数据
print(df.loc['20180819'])
'''
A 0
B 1
C 2
D 3
Name: 2018-08-19 00:00:00, dtype: int32
'''
# 指定列
# 两种方式
print(df.loc[:,'A':'B'])
print(df.loc[:,['A','B']])
'''
A B
2018-08-19 0 1
2018-08-20 4 5
2018-08-21 8 9
2018-08-22 12 13
2018-08-23 16 17
2018-08-24 20 21
'''
# 行与列同时检索
print(df.loc['20180819',['A','B']])
'''
A 0
B 1
Name: 2018-08-19 00:00:00, dtype: int32
'''
# 根据序列iloc
# 获取特定位置的值
print(df.iloc[3,1])
print(df.iloc[3:5,1:3]) # 不包含末尾5或3，同列表切片
'''
B C
2018-08-22 13 14
2018-08-23 17 18
'''
# 跨行操作
print(df.iloc[[1,3,5],1:3])
'''
B C
2018-08-20 5 6
2018-08-22 13 14
2018-08-24 21 22
'''
# 混合选择
print(df.ix[:3,['A','C']])
'''
A C
2018-08-19 0 2
2018-08-20 4 6
2018-08-21 8 10
'''
print(df.iloc[:3,[0,2]]) # 结果同上
# 通过判断的筛选
print(df[df.A>8])
'''
A B C D
2018-08-22 12 13 14 15
2018-08-23 16 17 18 19
2018-08-24 20 21 22 23
'''
3.2 筛选总结
1.iloc与ix区别
总结:相同点：iloc可以取相应的值，操作方便,与ix操作类似。
不同点：ix可以混合选择，可以填入column对应的字符选择，而iloc只能采用index索引，对于列数较多情况下，ix要方便操作许多。
2.loc与iloc区别
总结：相同点：都可以索引处块数据
不同点：iloc可以检索对应值,两者操作不同。
3.ix与loc、iloc三者的区别
总结：ix是混合loc与iloc操作
如下:对比三者操作
print(df.loc['20180819','A':'B'])
print(df.iloc[0,0:2])
print(df.ix[0,'A':'B'])
输出结果相同，均为:
A 0
B 1
Name: 2018-08-19 00:00:00, dtype: int32
4.Pandas设置值
4.1 创建数据
import pandas as pd
import numpy as np
# 创建数据
dates = pd.date_range('20180820',periods=6)
df = pd.DataFrame(np.arange(24).reshape(6,4), index=dates, columns=['A','B','C','D'])
print(df)
'''
A B C D
2018-08-20 0 1 2 3
2018-08-21 4 5 6 7
2018-08-22 8 9 10 11
2018-08-23 12 13 14 15
2018-08-24 16 17 18 19
2018-08-25 20 21 22 23
'''
4.2 根据位置设置loc和iloc
# 根据位置设置loc和iloc
df.iloc[2,2] = 111
df.loc['20180820','B'] = 2222
print(df)
'''
A B C D
2018-08-20 0 2222 2 3
2018-08-21 4 5 6 7
2018-08-22 8 9 111 11
2018-08-23 12 13 14 15
2018-08-24 16 17 18 19
2018-08-25 20 21 22 23
'''
4.3 根据条件设置
# 根据条件设置
# 更改B中的数，而更改的位置取决于4的位置，并设相应位置的数为0
df.B[df.A>4] = 0
print(df)
'''
A B C D
2018-08-20 0 2222 2 3
2018-08-21 4 5 6 7
2018-08-22 8 0 111 11
2018-08-23 12 0 14 15
2018-08-24 16 0 18 19
2018-08-25 20 0 22 23
'''
4.4 按行或列设置
# 按行或列设置
# 列批处理，F列全改为NaN
df['F'] = np.nan
print(df)
4.5 添加Series序列(长度必须对齐)
df['E'] = pd.Series([1,2,3,4,5,6], index=pd.date_range('20180820',periods=6))
print(df)
4.6 设定某行某列为特定值
# 设定某行某列为特定值
df.ix['20180820','A'] = 56
df.loc['20180820','A'] = 67
df.iloc[0,0] = 76
4.7 修改一整行数据
# 修改一整行数据
df.iloc[1] = np.nan # df.iloc[1,:]=np.nan
df.loc['20180820'] = np.nan # df.loc['20180820,:']=np.nan
df.ix[2] = np.nan # df.ix[2,:]=np.nan
df.ix['20180823'] = np.nan
print(df)
5.Pandas处理丢失数据
5.1 创建含NaN的矩阵
# Pandas处理丢失数据
import pandas as pd
import numpy as np
# 创建含NaN的矩阵
# 如何填充和删除NaN数据?
dates = pd.date_range('20180820',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns=['A','B','C','D']) # a.reshape(6,4)等价于a.reshape((6,4))
df.iloc[0,1] = np.nan
df.iloc[1,2] = np.nan
print(df)
'''
A B C D
2018-08-20 0 NaN 2.0 3
2018-08-21 4 5.0 NaN 7
2018-08-22 8 9.0 10.0 11
2018-08-23 12 13.0 14.0 15
2018-08-24 16 17.0 18.0 19
2018-08-25 20 21.0 22.0 23
'''
5.2 删除掉有NaN的行或列
# 删除掉有NaN的行或列
print(df.dropna()) # 默认是删除掉含有NaN的行
print(df.dropna(
axis=0, # 0对行进行操作;1对列进行操作
how='any' # 'any':只要存在NaN就drop掉；'all':必须全部是NaN才drop
))
'''
A B C D
2018-08-22 8 9.0 10.0 11
2018-08-23 12 13.0 14.0 15
2018-08-24 16 17.0 18.0 19
2018-08-25 20 21.0 22.0 23
'''
# 删除掉所有含有NaN的列
print(df.dropna(
axis=1,
how='any'
))
'''
A D
2018-08-20 0 3
2018-08-21 4 7
2018-08-22 8 11
2018-08-23 12 15
2018-08-24 16 19
2018-08-25 20 23
'''
5.3 替换NaN值为0或者其他
# 替换NaN值为0或者其他
print(df.fillna(value=0))
'''
A B C D
2018-08-20 0 0.0 2.0 3
2018-08-21 4 5.0 0.0 7
2018-08-22 8 9.0 10.0 11
2018-08-23 12 13.0 14.0 15
2018-08-24 16 17.0 18.0 19
2018-08-25 20 21.0 22.0 23
'''
5.4 是否有缺失数据NaN
# 是否有缺失数据NaN
# 是否为空
print(df.isnull())
'''
A B C D
2018-08-20 False True False False
2018-08-21 False False True False
2018-08-22 False False False False
2018-08-23 False False False False
2018-08-24 False False False False
2018-08-25 False False False False
'''
# 是否为NaN
print(df.isna())
'''
A B C D
2018-08-20 False True False False
2018-08-21 False False True False
2018-08-22 False False False False
2018-08-23 False False False False
2018-08-24 False False False False
2018-08-25 False False False False
'''
# 检测某列是否有缺失数据NaN
print(df.isnull().any())
'''
A False
B True
C True
D False
dtype: bool
'''
# 检测数据中是否存在NaN,如果存在就返回True
print(np.any(df.isnull())==True)
6.Pandas导入导出
6.1 导入数据
import pandas as pd # 加载模块
# 读取csv
data = pd.read_csv('student.csv')
# 打印出data
print(data)
# 前三行
print(data.head(3))
# 后三行
print(data.tail(3))
6.2 导出数据
# 将资料存取成pickle
data.to_pickle('student.pickle')
# 读取pickle文件并打印
print(pd.read_pickle('student.pickle'))
7.Pandas合并操作
7.1 Pandas合并concat
import pandas as pd
import numpy as np
# 定义资料集
df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'])
df2 = pd.DataFrame(np.ones((3,4))*1, columns=['a','b','c','d'])
df3 = pd.DataFrame(np.ones((3,4))*2, columns=['a','b','c','d'])
print(df1)
'''
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
'''
print(df2)
'''
a b c d
0 1.0 1.0 1.0 1.0
1 1.0 1.0 1.0 1.0
2 1.0 1.0 1.0 1.0
'''
print(df3)
'''
a b c d
0 2.0 2.0 2.0 2.0
1 2.0 2.0 2.0 2.0
2 2.0 2.0 2.0 2.0
'''
# concat纵向合并
res = pd.concat([df1,df2,df3],axis=0)
# 打印结果
print(res)
'''
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
0 1.0 1.0 1.0 1.0
1 1.0 1.0 1.0 1.0
2 1.0 1.0 1.0 1.0
0 2.0 2.0 2.0 2.0
1 2.0 2.0 2.0 2.0
2 2.0 2.0 2.0 2.0
'''
# 上述合并过程中，index重复，下面给出重置index方法
# 只需要将index_ignore设定为True即可
res = pd.concat([df1,df2,df3],axis=0,ignore_index=True)
# 打印结果
print(res)
'''
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
5 1.0 1.0 1.0 1.0
6 2.0 2.0 2.0 2.0
7 2.0 2.0 2.0 2.0
8 2.0 2.0 2.0 2.0
'''
# join 合并方式
#定义资料集
df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'], index=[1,2,3])
df2 = pd.DataFrame(np.ones((3,4))*1, columns=['b','c','d','e'], index=[2,3,4])
print(df1)
print(df2)
'''
join='outer',函数默认为join='outer'。此方法是依照column来做纵向合并，有相同的column上下合并在一起，
其他独自的column各自成列，原来没有值的位置皆为NaN填充。
'''
# 纵向"外"合并df1与df2
res = pd.concat([df1,df2],axis=0,join='outer')
print(res)
'''
a b c d e
1 0.0 0.0 0.0 0.0 NaN
2 0.0 0.0 0.0 0.0 NaN
3 0.0 0.0 0.0 0.0 NaN
2 NaN 1.0 1.0 1.0 1.0
3 NaN 1.0 1.0 1.0 1.0
4 NaN 1.0 1.0 1.0 1.0
'''
# 修改index
res = pd.concat([df1,df2],axis=0,join='outer',ignore_index=True)
print(res)
'''
a b c d e
0 0.0 0.0 0.0 0.0 NaN
1 0.0 0.0 0.0 0.0 NaN
2 0.0 0.0 0.0 0.0 NaN
3 NaN 1.0 1.0 1.0 1.0
4 NaN 1.0 1.0 1.0 1.0
5 NaN 1.0 1.0 1.0 1.0
'''
# join='inner'合并相同的字段
# 纵向"内"合并df1与df2
res = pd.concat([df1,df2],axis=0,join='inner')
# 打印结果
print(res)
'''
b c d
1 0.0 0.0 0.0
2 0.0 0.0 0.0
3 0.0 0.0 0.0
2 1.0 1.0 1.0
3 1.0 1.0 1.0
4 1.0 1.0 1.0
'''
# join_axes(依照axes合并)
#定义资料集
df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'], index=[1,2,3])
df2 = pd.DataFrame(np.ones((3,4))*1, columns=['b','c','d','e'], index=[2,3,4])
print(df1)
'''
a b c d
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0
'''
print(df2)
'''
b c d e
2 1.0 1.0 1.0 1.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
'''
# 依照df1.index进行横向合并
res = pd.concat([df1,df2],axis=1,join_axes=[df1.index])
print(res)
'''
a b c d b c d e
1 0.0 0.0 0.0 0.0 NaN NaN NaN NaN
2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
'''
# 移除join_axes参数,打印结果
res = pd.concat([df1,df2],axis=1)
print(res)
'''
a b c d b c d e
1 0.0 0.0 0.0 0.0 NaN NaN NaN NaN
2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
'''
# append(添加数据)
# append只有纵向合并，没有横向合并
#定义资料集
df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'])
df2 = pd.DataFrame(np.ones((3,4))*1, columns=['a','b','c','d'])
df3 = pd.DataFrame(np.ones((3,4))*2, columns=['a','b','c','d'])
s1 = pd.Series([1,2,3,4], index=['a','b','c','d'])
# 将df2合并到df1下面,以及重置index,并打印出结果
res = df1.append(df2,ignore_index=True)
print(res)
'''
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
5 1.0 1.0 1.0 1.0
'''
# 合并多个df,将df2与df3合并至df1的下面,以及重置index,并打印出结果
res = df1.append([df2,df3], ignore_index=True)
print(res)
'''
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
5 1.0 1.0 1.0 1.0
6 2.0 2.0 2.0 2.0
7 2.0 2.0 2.0 2.0
8 2.0 2.0 2.0 2.0
'''
# 合并series,将s1合并至df1，以及重置index，并打印结果
res = df1.append(s1,ignore_index=True)
print(res)
'''
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 1.0 2.0 3.0 4.0
'''
# 总结:两种常用合并方式
res = pd.concat([df1, df2, df3], axis=0, ignore_index=True)
res1 = df1.append([df2, df3], ignore_index=True)
print(res)
print(res1)
7.2.Pandas 合并 merge
7.2.1 定义资料集并打印出
import pandas as pd
# 依据一组key合并
# 定义资料集并打印出
left = pd.DataFrame({'key' : ['K0','K1','K2','K3'],
'A' : ['A0','A1','A2','A3'],
'B' : ['B0','B1','B2','B3']})
right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
'C' : ['C0', 'C1', 'C2', 'C3'],
'D' : ['D0', 'D1', 'D2', 'D3']})
print(left)
'''
A B key
0 A0 B0 K0
1 A1 B1 K1
2 A2 B2 K2
3 A3 B3 K3
'''
print(right)
'''
C D key
0 C0 D0 K0
1 C1 D1 K1
2 C2 D2 K2
3 C3 D3 K3
'''
7.2.2 依据key column合并,并打印
# 依据key column合并,并打印
res = pd.merge(left,right,on='key')
print(res)
'''
A B key C D
0 A0 B0 K0 C0 D0
1 A1 B1 K1 C1 D1
2 A2 B2 K2 C2 D2
3 A3 B3 K3 C3 D3
'''
# 依据两组key合并
#定义资料集并打印出
left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
'key2': ['K0', 'K1', 'K0', 'K1'],
'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3']})
right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
'key2': ['K0', 'K0', 'K0', 'K0'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']})
print(left)
'''
A B key1 key2
0 A0 B0 K0 K0
1 A1 B1 K0 K1
2 A2 B2 K1 K0
3 A3 B3 K2 K1
'''
print(right)
'''
C D key1 key2
0 C0 D0 K0 K0
1 C1 D1 K1 K0
2 C2 D2 K1 K0
3 C3 D3 K2 K0
'''
7.2.3 两列合并
依据key1与key2 columns进行合并
# 依据key1与key2 columns进行合并，并打印出四种结果['left', 'right', 'outer', 'inner']
res = pd.merge(left, right, on=['key1', 'key2'], how='inner')
print(res)
res = pd.merge(left, right, on=['key1', 'key2'], how='outer')
print(res)
res = pd.merge(left, right, on=['key1', 'key2'], how='left')
print(res)
res = pd.merge(left, right, on=['key1', 'key2'], how='right')
print(res)
'''
---------------inner方式---------------
A B key1 key2 C D
0 A0 B0 K0 K0 C0 D0
1 A2 B2 K1 K0 C1 D1
2 A2 B2 K1 K0 C2 D2
---------------outer方式---------------
A B key1 key2 C D
0 A0 B0 K0 K0 C0 D0
1 A1 B1 K0 K1 NaN NaN
2 A2 B2 K1 K0 C1 D1
3 A2 B2 K1 K0 C2 D2
4 A3 B3 K2 K1 NaN NaN
5 NaN NaN K2 K0 C3 D3
---------------left方式---------------
A B key1 key2 C D
0 A0 B0 K0 K0 C0 D0
1 A1 B1 K0 K1 NaN NaN
2 A2 B2 K1 K0 C1 D1
3 A2 B2 K1 K0 C2 D2
4 A3 B3 K2 K1 NaN NaN
--------------right方式---------------
A B key1 key2 C D
0 A0 B0 K0 K0 C0 D0
1 A2 B2 K1 K0 C1 D1
2 A2 B2 K1 K0 C2 D2
3 NaN NaN K2 K0 C3 D3
'''
7.2.4 Indicator设置合并列名称
# Indicator
df1 = pd.DataFrame({'col1':[0,1],'col_left':['a','b']})
df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})
print(df1)
'''
col1 col_left
0 0 a
1 1 b
'''
print(df2)
'''
col1 col_right
0 1 2
1 2 2
2 2 2
'''
# 依据col1进行合并,并启用indicator=True,最后打印
res = pd.merge(df1,df2,on='col1',how='outer',indicator=True)
print(res)
'''
col1 col_left col_right _merge
0 0 a NaN left_only
1 1 b 2.0 both
2 2 NaN 2.0 right_only
3 2 NaN 2.0 right_only
'''
# 自定义indicator column的名称,并打印出
res = pd.merge(df1,df2,on='col1',how='outer',indicator='indicator_column')
print(res)
'''
col1 col_left col_right indicator_column
0 0 a NaN left_only
1 1 b 2.0 both
2 2 NaN 2.0 right_only
3 2 NaN 2.0 right_only
'''
7.2.5 依据index合并
# 依据index合并
#定义资料集并打印出
left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=['K0', 'K1', 'K2'])
right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],
'D': ['D0', 'D2', 'D3']},
index=['K0', 'K2', 'K3'])
print(left)
'''
A B
K0 A0 B0
K1 A1 B1
K2 A2 B2
'''
print(right)
'''
C D
K0 C0 D0
K2 C2 D2
K3 C3 D3
'''
# 依据左右资料集的index进行合并,how='outer',并打印
res = pd.merge(left,right,left_index=True,right_index=True,how='outer')
print(res)
'''
A B C D
K0 A0 B0 C0 D0
K1 A1 B1 NaN NaN
K2 A2 B2 C2 D2
K3 NaN NaN C3 D3
'''
# 依据左右资料集的index进行合并,how='inner',并打印
res = pd.merge(left,right,left_index=True,right_index=True,how='inner')
print(res)
'''
A B C D
K0 A0 B0 C0 D0
K2 A2 B2 C2 D2
'''
7.2.6 解决overlapping的问题
# 解决overlapping的问题
#定义资料集
boys = pd.DataFrame({'k': ['K0', 'K1', 'K2'], 'age': [1, 2, 3]})
girls = pd.DataFrame({'k': ['K0', 'K0', 'K3'], 'age': [4, 5, 6]})
print(boys)
'''
age k
0 1 K0
1 2 K1
2 3 K2
'''
print(girls)
'''
age k
0 4 K0
1 5 K0
2 6 K3
'''
# 使用suffixes解决overlapping的问题
# 比如将上面两个合并时,age重复了,则可通过suffixes设置,以此保证不重复,不同名
res = pd.merge(boys,girls,on='k',suffixes=['_boy','_girl'],how='inner')
print(res)
'''
age_boy k age_girl
0 1 K0 4
1 1 K0 5
'''
8.Pandas plot出图
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data = pd.Series(np.random.randn(1000), index=np.arange(1000))
print(data)
print(data.cumsum())
# data本来就是一个数据，所以我们可以直接plot
data.plot()
plt.show()
# np.random.randn(1000,4) 随机生成1000行4列数据
# list("ABCD")会变为['A','B','C','D']
data = pd.DataFrame(
np.random.randn(1000,4),
index=np.arange(1000),
columns=list("ABCD")
)
data.cumsum()
data.plot()
plt.show()
ax = data.plot.scatter(x='A',y='B',color='DarkBlue',label='Class1')
# 将之下这个 data 画在上一个 ax 上面
data.plot.scatter(x='A',y='C',color='LightGreen',label='Class2',ax=ax)
plt.show()
9.学习来源
1.https://morvanzhou.github.io/tutorials/data-manipulation/np-pd/ 2.https://morvanzhou.github.io/tutorials/data-manipulation/np-pd/
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原始发表：2019-01-15，如有侵权请联系 cloudcommunity@tencent.com 删除
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